Jira to Panoply

This page provides you with instructions on how to extract data from Jira and load it into Panoply. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Jira?

Atlassian's Jira is an issue-tracking tool with collaboration and elements of agile project management woven into it. You can track progress, assign tasks, and introduce results all from within the product.

What is Panoply?

Panoply provides a Smart Cloud Data Warehouse platform that lets users set up a new Amazon Redshift instance in just a few clicks. It uses machine learning algorithms to accomplish complex tasks like schema building, data mining, modeling, scaling, performance tuning, security, and backup. Panoply can import data with no schema, no modeling, and no configuration, and you can use analysis, SQL, and visualization tools on data in Panoply just as you would if you were creating a Redshift data warehouse on your own.

Getting data out of Jira

You can get your data out of Jira by using Jira's REST API, which offers access to issues, comments, and numerous other endpoints. For example, to get data about an issue,
you could call GET /rest/api/2/issue/[issueIdOrKey].

Preparing Jira data

Once you have the JSON in hand, you need to map the data fields into a schema that can be inserted into your database. This means that, for each value in the response, you need to identify a predefined datatype (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them.

Check out the Stitch Jira Documentation to get a sense of what fields and datatypes are provided by each endpoint. Once you've identified all of the columns you want to insert, you can create a destination table in your database into which to load the data.

Loading data into Panoply

Once you've identified all the columns you want to insert, you can use Redshift's CREATE TABLE statement to create a table to receive all of the data.

Once you have a table built, you might think that the easiest way to migrate your data (especially if there isn't much of it) would be to build INSERT statements to add data to your Redshift table row by row. Don't do it! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, we suggest loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

Keeping Jira data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Jira.

And remember, as with any code, once you write it, you have to maintain it. If Atlassian modifies Jira's API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

Other data warehouse options

Panoply is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, and To Snowflake.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Jira data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Panoply data warehouse.